Generalized Expected Value Model Based on Compound Quantification and Its Application in Transportation Problems

As a kind of particular programming, transportation problem draws much attention in many fields such as energy development, materials management, etc. In this paper, by analyzing the essence of stochastic programming and the deficiencies of existing methods, we propose a comparison method based on synthesizing effect for the standard to judge an objective value is good or not, and give the axiom system of stochastic synthesizing effect function, further, we introduce a quasi-linear pattern based on expectation and variance for the satisfaction of random constraints. Moreover, using synthesizing effect and quasi-linear pattern, we establish a stochastic programming pattern (generalized expected value model, and denoted by GEM for short) with good operability, and for the stochastic transportation problem, we also establish its corresponding generalized expected value model. Finally, we analyze the performance of GEM by combining with a transportation case under random environment. All these indicate that GEM includes the existing methods, and it can effectively solve the stochastic transportation problem under complex environment or with incomplete information, in addition, GEM can merge decision consciousness into solution through quantitative way.